牛的健康状况分类使用AWS云上的机器学习和数据管理
Kristina Dineva1, Tatiana Atanasova1
1Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 2, 1113 Sofia, Bulgaria.
Animals : an open access journal from MDPI
|October 28, 2023
概括
机器学习 (ML) 模型使用物联网数据准确地分类牛的健康状况. 该系统通过早期检测健康问题来提高牲畜福利和农场利能力.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 兽医医学 兽医医学 兽医医学
背景情况:
- 牲畜健康对于农业的可持续性和利性至关重要.
- 有效监测个体牛的健康状况对于预防疾病爆发和保持牛群生产力至关重要.
- 非侵入性监测技术为实时健康评估提供了一个有希望的方法.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于将奶牛的健康状况分为三个类别.
- 建立一个系统的数据处理和ML建模工作流程,用于奶牛场应用.
- 将ML模型集成到基于云的架构中,以便在现实世界中部署.
主要方法:
- 从奶牛农场的非侵入性物联网设备收集数据,包括环境和个人奶牛指标 (年龄,哺乳状态).
- 系统化数据处理工作流程用于清洁,特征工程和模型培训.
- 培训和评估五个ML算法,重点是随机森林分类器 (RFC).
- 在亚马逊网络服务 (AWS) 上开发云架构,用于模型部署和客户端应用程序界面,用于结果可视化.
主要成果:
- 随机森林分类器 (RFC) 在健康状况评估中取得了最高的表现.
- RFC模型显示了高精度 (0.959),回忆 (0.954) 和精度 (0.97).
- 成功实施了全面的数据处理工作流和云集成策略.
结论:
- 开发的ML模型,特别是RFC,有效地对奶牛的健康状况进行了分类,为奶牛养殖场管理提供了有价值的工具.
- 拟议的工作流和云架构为实时畜牧健康监测提供了可扩展和可靠的解决方案.
- 这种方法有助于改善动物福利,提高农场生产力,提高农业可持续性.
关键词:
亚马逊网络服务 (AWS)奶牛牛奶牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛数据分析数据分析数据分析数据集成数据集成数据集成数据建模数据建模健康状况分类健康状况分类整合模型的整合模型.随机森林分类器 (RFC) 是一个随机森林分类器.更多相关视频
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